Allergies and Asthma: Employing principles of social justice as a guide in public health policy development
Bibliographic record
Abstract
The growing epidemic of allergy and allergy-induced asthma poses a significant challenge to population health. This article, written for a target audience of policy-makers in public health, aims to contribute to the development of policies to counter allergy morbidities by demonstrating how principles of social justice can guide public health initiatives in reducing allergy and asthma triggers. Following a discussion of why theories of social justice have utility in analyzing allergy, a step-wise policy assessment protocol formulated on Rawlsian principles of social justice is presented. This protocol can serve as a tool to aid in prioritizing public health initiatives and identifying ethically problematic policies that necessitate reform. Criteria for policy assessment include: 1) whether a tentative public health intervention would provide equal health benefit to a range of allergy and asthma sufferers, 2) whether targeting initiatives towards particular societal groups is merited based on the notion of ‘worst-off status’ of certain population segments, and 3) whether targeted policies have the potential for stigmatization. The article concludes by analyzing three examples of policies used in reducing allergy and asthma triggers in order to convey the general thought process underlying the use of the assessment protocol, which public health officials could replicate as a guide in actual, region-specific policy development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.207 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.016 | 0.116 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".